diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index 4e78378..e5aff2e 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -24,6 +24,6 @@ repos:
         types: ["python"]
       - id: pip-audit
         name: pip-audit
-        entry: pipenv run pip-audit --ignore-vuln GHSA-4xh5-x5gv-qwph
+        entry: pipenv run pip-audit
         language: system
         pass_filenames: false
\ No newline at end of file
diff --git a/Makefile b/Makefile
index 6b86185..c5882af 100644
--- a/Makefile
+++ b/Makefile
@@ -46,7 +46,7 @@ ruff: # Run 'ruff' linter and print a preview of errors
 	pipenv run ruff check .
 
 safety: # Check for security vulnerabilities and verify Pipfile.lock is up-to-date
-	pipenv run pip-audit --ignore-vuln GHSA-4xh5-x5gv-qwph
+	pipenv run pip-audit
 	pipenv verify
 
 lint-apply: black-apply ruff-apply # Apply changes with 'black' and resolve 'fixable errors' with 'ruff'
diff --git a/Pipfile b/Pipfile
index eb1ab8b..b27d3c5 100644
--- a/Pipfile
+++ b/Pipfile
@@ -6,11 +6,11 @@ name = "pypi"
 [packages]
 attrs = "*"
 boto3 = "*"
+duckdb = "==1.4.2"
+duckdb-engine = "*"
 pandas = "*"
 pyarrow = "*"
 sqlalchemy = "*"
-duckdb-engine = "*"
-duckdb = "==1.4.2.dev27"
 
 [dev-packages]
 black = "*"
diff --git a/Pipfile.lock b/Pipfile.lock
index a6ae3f7..31b1f44 100644
--- a/Pipfile.lock
+++ b/Pipfile.lock
@@ -1,7 +1,7 @@
 {
     "_meta": {
         "hash": {
-            "sha256": "725d3ad7f9e32ba05555a9175511711f35d062ca5f66959246ea4c4d412e51c6"
+            "sha256": "9f37fdf15828cfc3364bb447dba4253f8c05c993116eae4083e2f8af427a8c32"
         },
         "pipfile-spec": 6,
         "requires": {
@@ -27,64 +27,64 @@
         },
         "boto3": {
             "hashes": [
-                "sha256:96ee720b52be647d8ef5ba92fccfce6b65d6321769430fe6edd10d57ec43c25b",
-                "sha256:ecc8c99d3cc96716cdfba62d9c9c6ce0eb98d02494a66690bcc2ec181c1ced67"
+                "sha256:41fc8844b37ae27b24bcabf8369769df246cc12c09453988d0696ad06d6aa9ef",
+                "sha256:484e46bf394b03a7c31b34f90945ebe1390cb1e2ac61980d128a9079beac87d4"
             ],
             "index": "pypi",
             "markers": "python_version >= '3.9'",
-            "version": "==1.40.52"
+            "version": "==1.40.74"
         },
         "botocore": {
             "hashes": [
-                "sha256:838697a06c7713df8d39f088105334b4eadcc3d65c7a260bf1a1bd8bf616ce4a",
-                "sha256:b65d970ca4ccd869639332083da17c3a933bcf495120dcc4f5c7723cb3f6216c"
+                "sha256:57de0b9ffeada06015b3c7e5186c77d0692b210d9e5efa294f3214df97e2f8ee",
+                "sha256:f39f5763e35e75f0bd91212b7b36120b1536203e8003cd952ef527db79702b15"
             ],
             "markers": "python_version >= '3.9'",
-            "version": "==1.40.52"
+            "version": "==1.40.74"
         },
         "duckdb": {
             "hashes": [
-                "sha256:055a31d715facbc8416ef01cbaad8e7c007a48f73733e1504593ece9870749e3",
-                "sha256:09f0c95022615bb6593240e9aded18c825b71a7708b4d5feac8010b6835a0218",
-                "sha256:0b5ed2dd2b5c7d8f8add6b90be1a75f782b2d947cad9b4ef180e93ba8fe2c712",
-                "sha256:0b900f801b05c42b7c0b38d0f8679c5a0cafdef9a4f5f5bd0c194a24fc94312f",
-                "sha256:2c2f363942232019fae10efd9b92c9dde590a955600898c6588a2abbbf322f5d",
-                "sha256:2e51c03335992d79190e9230a8bb69d5504e22a0705e6057bbc454cf4742db18",
-                "sha256:3141afe3e59a238bd79accb3b5b505ad6bde7e648a67ecb32a87d29baa1d507c",
-                "sha256:33bde9de3b5c1a70d6ba212821afaad5d49de2644c85ae021bac9c8d36d358c2",
-                "sha256:352afa65795588a540d414c1fd4f3aa125c320c524f26f6d9017788a7efd6245",
-                "sha256:359fd08fa84c7ad303334778b3d20c1ef391bf0a7523753f5b4fc582eb63e309",
-                "sha256:409a2dea728953a4d7500fcbe4f96d5e021cc996cb73c2aa45d788fbcfc2ecac",
-                "sha256:4457538386e403cb12efdfb1cd3a1cf4a47b3947e5b46dc6cd6e306af23f5ad4",
-                "sha256:451534c2a51f6d9ffb44937605b4db2226e39664520c548bd9ed090060d14c54",
-                "sha256:4b9036e19b51efca43b2cfc639810d4db7c4a75c42132c7c6fee946f1d0e194b",
-                "sha256:50458f77161f28928dc805578b13e874b883e1e67e686c7173ae629f01b951e9",
-                "sha256:569fe3ecb5ee537cafe3b5823a3798e39b9b7058a374231858beb85e8346c509",
-                "sha256:600f886e72d3efbce6629e696d95d9401d012664b547239d0957372d08b32309",
-                "sha256:69872aec744cde965cd1ec257cbc275867b40c26e154cef18916c2b219a351b6",
-                "sha256:6fe0089bb487a8b47c5b7a189cf83c186faf2da95848cc9d892a5e3384f6ff91",
-                "sha256:70bf6ad12dd103d0e2babacb8167af243b08987b5ef6806bf0f68fcd27493c03",
-                "sha256:727094fc2ee39ac2227d7cd16c6cf5eacd747c0303d9a4b8c9b8e8a7f31bcbfd",
-                "sha256:753dbc58f1be72fbd111882fcb40a3ca3a11183b1fae70ee3d9ee7314312448e",
-                "sha256:7947440cb61279b7f6acdc624f0c72d700b3591f178dd3c064d4fe40f257fc85",
-                "sha256:844ca95c1a9df1fab6b45a4be77e867f4b1663d74813ba2695a995dba59f4012",
-                "sha256:8f9685dc88a18d5a47157a4ea1e5bc3e069cde42356f959650897369db6eef15",
-                "sha256:934464e710ee057c9e43ed2eee60e8fad207be9fe387cf21dd42e05fde9c61f9",
-                "sha256:94b2c9c8d2e1b04452534f3e6988d6752b462c55ead10c3b2ecd746e9c9dc604",
-                "sha256:9e3190bb3e696d8380ccc68ef77ca982244f5debc2cad64aaceb83f14a57438e",
-                "sha256:d5b51c838a2a6d3eec537e3852d432d83d1c213fc14431760734c0b5a8c86d7b",
-                "sha256:da0857a3f1bb3c4e4f8ceee9028dff7b6738c60dca9256b43861b1ab17fbdaf7",
-                "sha256:dc06efeba54e0e8bc9af4ab0abbbb6037e0f96f49c98d1bb93a878df70ec0158",
-                "sha256:dfd79a499d2d7d6447c5d31db2781fbd11c80e3b1834232a84f3aa08cfb3f329",
-                "sha256:e03a3b89dcf69e9f41ce0f512a307bccb1818aa2e8ebd6e98f36cc1360c20a9f",
-                "sha256:e130343c5d7fc87dff9cd002f7ec7791e9e76020bd23a47d0ac2ba6775b9fb91",
-                "sha256:e1dbe3ed4e6a11bf93424f42d73f1428c12d9fdaf724996dfee8982299a5a9e4",
-                "sha256:f7b5c1a0809e0123f4b8cc18b749d573f6e96f33472f5486283df2afd802a3f1",
-                "sha256:f8ea19fbb6b5ee327c9485273989fbcdbd32f5fc3ce97058aeaf3cd020284ac3"
+                "sha256:0c2315b693f201787c9892f31eb9a0484d3c648edb3578a86dc8c1284dd2873a",
+                "sha256:125cd89dbfd40846f216032b11e5eeaf2be13ee4d6745b82413ddd213ddc4d99",
+                "sha256:128c97dab574a438d7c8d020670b21c68792267d88e65a7773667b556541fa9b",
+                "sha256:19d2c2f3cdf0242cad42e803602bbc2636706fc1d2d260ffac815ea2e3a018e8",
+                "sha256:20c45b4ead1ea4d23a1be1cd4f1dfc635e58b55f0dd11e38781369be6c549903",
+                "sha256:2d3c39429b3ce1ee33d86daa94bed75a1f5b0fcf4d66d0839a6fcee398894548",
+                "sha256:2e552451054534970dc999e69ca5ae5c606458548c43fb66d772117760485096",
+                "sha256:2f7c61617d2b1da3da5d7e215be616ad45aa3221c4b9e2c4d1c28ed09bc3c1c4",
+                "sha256:389fa9abe4ca37d091332a2f8c3ebd713f18e87dc4cb5e8efd3e5aa8ddf8885f",
+                "sha256:422be8c6bdc98366c97f464b204b81b892bf962abeae6b0184104b8233da4f19",
+                "sha256:43ac632f40ab1aede9b4ce3c09ea043f26f3db97b83c07c632c84ebd7f7c0f4a",
+                "sha256:449fa37cdefa85bf925681e551157f3cb6434ee85c8329d2f72d52e432e8810f",
+                "sha256:459b1855bd06a226a2838da4f14c8863fd87a62e63d414a7f7f682a7c616511a",
+                "sha256:4d757dae8c63eeb001517ce4cfba768f87f90628c5d22f230773c1fe0b430c5c",
+                "sha256:4da7aafa94800f475d287814ad91993cf1f912c16f76ff4b411769da40c4b7da",
+                "sha256:769440f4507c20542ae2e5b87f6c6c6d3f148c0aa8f912528f6c97e9aedf6a21",
+                "sha256:77db030b48321bf785767b7b1800bf657dd2584f6df0a77e05201ecd22017da2",
+                "sha256:7a496a04458590dcec8e928122ebe2ecbb42c3e1de4119f5461f7bf547acbe79",
+                "sha256:7be8c0c40f2264b91500b89c688f743e1c7764966e988f680b1f19416b00052e",
+                "sha256:85f0c36c1b5f378d96dd7d8c6d312317f4f547a567e8b76cacb2590a31d931f3",
+                "sha256:9106c7292cff824e9497233bb16867ab3bca82885a11f1b533e7ecabf8073b34",
+                "sha256:9356fe17af2711e0a5ace4b20a0373e03163545fd7516e0c3c40428f44597052",
+                "sha256:946a8374c0252db3fa41165ab9952b48adc8de06561a6b5fd62025ac700e492f",
+                "sha256:995ec9c1fc3ce5fbfe5950b980ede2a9d51b35fdf2e3f873ce94c22fc3355fdc",
+                "sha256:a456adbc3459c9dcd99052fad20bd5f8ef642be5b04d09590376b2eb3eb84f5c",
+                "sha256:b2d882672b61bc6117a2c524cf64ea519d2e829295951d214f04e126f1549b09",
+                "sha256:bdd2d808806ceeeec33ba89665a0bb707af8815f2ca40e6c4c581966c0628ba1",
+                "sha256:c45e0e682ee9073c36dc34d7ad8033210bfea0cab80cc98d1eca516227b35fdf",
+                "sha256:c5feb658ed728cfc42d6fe5ff4e2035faf438a3f10c875f3cfa39ef9e2a2c004",
+                "sha256:c6a21732dd52a76f1e61484c06d65800b18f57fe29e8102a7466c201a2221604",
+                "sha256:c6d41fea4f9038663e6b9c325075a843fd105eaff0ec3d5fe31dfa9014114c3e",
+                "sha256:d4af73198e56ba3bc1b2a05eaaf93d162615c225c031685f2a20ef6b2798ed33",
+                "sha256:de646227fc2c53101ac84e86e444e7561aa077387aca8b37052f3803ee690a17",
+                "sha256:df81acee3b15ecb2c72eb8f8579fb5922f6f56c71f5c8892ea3bc6fab39aa2c4",
+                "sha256:dfcc56a83420c0dec0b83e97a6b33addac1b7554b8828894f9d203955591218c",
+                "sha256:e1c80934cb15879844a752776a1ea3d1405635f307f5bb8b87c99f5a5564d33a",
+                "sha256:f1fac31babda2045d4cdefe6d0fd2ebdd8d4c2a333fbcc11607cfeaec202d18d"
             ],
             "index": "pypi",
             "markers": "python_full_version >= '3.9.0'",
-            "version": "==1.4.2.dev27"
+            "version": "==1.4.2"
         },
         "duckdb-engine": {
             "hashes": [
@@ -105,83 +105,83 @@
         },
         "numpy": {
             "hashes": [
-                "sha256:035796aaaddfe2f9664b9a9372f089cfc88bd795a67bd1bfe15e6e770934cf64",
-                "sha256:043885b4f7e6e232d7df4f51ffdef8c36320ee9d5f227b380ea636722c7ed12e",
-                "sha256:04a69abe45b49c5955923cf2c407843d1c85013b424ae8a560bba16c92fe44a0",
-                "sha256:0f2bcc76f1e05e5ab58893407c63d90b2029908fa41f9f1cc51eecce936c3365",
-                "sha256:13b9062e4f5c7ee5c7e5be96f29ba71bc5a37fed3d1d77c37390ae00724d296d",
-                "sha256:15eea9f306b98e0be91eb344a94c0e630689ef302e10c2ce5f7e11905c704f9c",
-                "sha256:15fb27364ed84114438fff8aaf998c9e19adbeba08c0b75409f8c452a8692c52",
-                "sha256:1b219560ae2c1de48ead517d085bc2d05b9433f8e49d0955c82e8cd37bd7bf36",
-                "sha256:22758999b256b595cf0b1d102b133bb61866ba5ceecf15f759623b64c020c9ec",
-                "sha256:2ec646892819370cf3558f518797f16597b4e4669894a2ba712caccc9da53f1f",
-                "sha256:3634093d0b428e6c32c3a69b78e554f0cd20ee420dcad5a9f3b2a63762ce4197",
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-                "sha256:e78aecd2800b32e8347ce49316d3eaf04aed849cd5b38e0af39f829a4e59f5eb",
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@@ -470,57 +481,58 @@
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@@ -741,189 +753,189 @@
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@@ -988,20 +1000,20 @@
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@@ -1148,11 +1160,11 @@
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@@ -1164,12 +1176,12 @@
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@@ -1286,59 +1298,59 @@
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-                "sha256:dc56bc708f2d8ac71bd1dcb927e458c93cec10b98eb4120206a4091db7b67b99",
-                "sha256:e563271e2c5ff4d4a4cbeb2c83d5cf0d4938b891518e676025f7268c6fe5fe26",
-                "sha256:e72a8ec6b868e258a2cd2672d91f2860ad532d590ce94cdf7d5e7ec674ccf03d",
-                "sha256:e99310a4ebd4479bcd1964dff9e14af33746300cb014aa4a3781738ac63baf4a",
-                "sha256:f522e5709379d72fb3da7785aa489ff0bb87448a9dc5a75f45763a795a089ebd",
-                "sha256:fc0d2f88b81dcf3ccf9a6ae17f89183762c8a94a5bdcfa09e05cfe413acf0503",
-                "sha256:fee33b0ca46f4c85443d6c450357101e47d53e6c3f008d658c27a2d020d44c79"
+                "sha256:001ea83a58024818826a9e3f89bf9310a114f7e26dfe404a4c32686f97bd7901",
+                "sha256:00626d9dc0f5ef3a75fe63fd68b9c7c8302d2b5bbc7f74ecaedba83447a24f84",
+                "sha256:0c34fe18094686194f204a3b1787a27456897d8a2d62caf84b61e8dfbc0252ae",
+                "sha256:12fe549c9b10ac98c91cf791d2945e878875d95508e1a5d14091a7aaa66d9cf8",
+                "sha256:1a812a5b727bc09c3d7ea072c4eebf657c2f7066155506ba31ebf4792f88f016",
+                "sha256:252be4a05f9d9185bb8c18e83764ebcfea7185076c07a7a662253af3a8c07941",
+                "sha256:334f900ff08ce0423407af97e6c26ad5d4e3b0763645559ece6fbf3747d6a8f5",
+                "sha256:35ad0f0378c9359b3f297299c3309778bb03b8612f987399a0333a560b43862d",
+                "sha256:3d600dc583260d845c7d8a6db540339dd883081925da2bd1c5cb808f720b3cd9",
+                "sha256:3e294c5eadfb93d78b0763e859a0c16d4051fc1c5231ae8956d61cb0b5666f5a",
+                "sha256:3e739edd001b04f654b166204fc7a9de896cf6007eaff33409ee9e50ceaff754",
+                "sha256:44729980b6c50a5f2bfcc2668d36c569ce17f8b17bccaf470c4313dcbbf13c9d",
+                "sha256:44d2d26cda26d18f7af7db71453b7b783788322d756e81730acb98f24eb90ace",
+                "sha256:4c19236ae2402a8663a2c8f21f1870a03cc57f0bef7e4b6eb3238cc82944de80",
+                "sha256:69763ab2445f632d90b504a815a2a033f74332997052b721002298ed6de40f2e",
+                "sha256:6dda1ddac033d27421c20d7a7943eec60be44e0db4e079f33cc5af3b8280ccde",
+                "sha256:6f9762274496c244d951c819348afbcf212714902742225f649cf02823a6a10f",
+                "sha256:710624ab925dc2b05a6229d47f6f0dac1c1155e6ed559be7109f684eba048a48",
+                "sha256:7388ac685cab5b279a41dfe0a6ccd99e4dbf322edfb63e02fc0443bf24134e91",
+                "sha256:77718810bd3066158db1e95a63c160ad7ce08c6b0710bc656055033e39cdad88",
+                "sha256:7a820d8ae11facf32585507c11f04e3f38343c1e784c9b5a8b1da5c930547fe2",
+                "sha256:8382ad21458075c2e66a82a29d650f963ce51c7708c7c0ff313a8c206c4fd5e8",
+                "sha256:84378110dd9a6c06323b41b56e129c504d157d1a983ce8f5443761eb5256bafc",
+                "sha256:854794239111d2b88b40b6ef92aa478024d1e5074f364033e73e21e3f76b25e0",
+                "sha256:92843c305330aa94a36e706c16209cd4df274693e777ca47112617db7d0ef3d7",
+                "sha256:9bddc2cade6561f6820d4cd73f99a0243532ad506bc510a75a5a65a522b2d74d",
+                "sha256:a4893d31e5ef780b6edcaf63122df0f8d321088bb0dee4c8c06eccb1ca28d145",
+                "sha256:a9d9ffdc2ab696f6b15b4d1f7cec6658e1d788124418cb30030afbae31c64746",
+                "sha256:ac93252226cf288753d8b46280f4edf3433bf9508b6977f8dd8526b521a1bbb9",
+                "sha256:b41f37cabfe2463232684de44bad753d6be08a7a072f6a83447eeaf0e4d2a215",
+                "sha256:b883fe6fd85adad7932b3271c38ac289c65b7337c2c132e9569f9d3940620730",
+                "sha256:b9d71701ce97c95480fecb0039ec5bb889e75f110da72005743451339262f4ce",
+                "sha256:ba95112d15fd4f1105fb2402c4eab9068f0554435e9b7085924bcfaac2cc306f",
+                "sha256:bba208d9c7decf9961998edf5c65e3ea4355d5818dd6cd0f6809bec1afb951cc",
+                "sha256:bd0d42297ace400d8febe55f13fdf46e86754842b860c978dfec16f081e5c653",
+                "sha256:bea79263d55c24a32b0d79c00a1c58bb2ee5f0757ed95656b01c0fb310c5af3d",
+                "sha256:c064e28361c05d72eed8e744c9605cbd6d2bb7481a511c74071fd9b24bc65d7d",
+                "sha256:c3200cb41cdbc65156e5f8c908d739b0dfed57e890329413da2748d1a2cd1a4e",
+                "sha256:c6c791b09c57ed76a18b03f2631753a4960eefbbca80f846da8baefc6491fcfe",
+                "sha256:c6ec3675d98915bf1ec8b3c7986422682f7232ea76cad276f4c8abd5b7319b70",
+                "sha256:ce20fe000754f477c8a9125543f1936ea5b8867c5406757c224d745ed033e691",
+                "sha256:cedb9dd9358e4ea1d9bce3665ce0797f6adf97ff142c8e25b46ba9cdd508e9b6",
+                "sha256:e0a15757fccb38c410947df156f9749ae4a3c89b2393741a50521f39a8cf202a",
+                "sha256:e6e95176209257803a8b3d0394f21604e796dadb643d2f7ca21b66c9c0b30c9a",
+                "sha256:e70ff90c64419709d38c8932ea9fe1cc98415c4f87ea8da81719e43f02534bc9",
+                "sha256:ec1a15968a9d80da01e1d30349b2b0d7cc91e96588ee324ce1b5228175043e95",
+                "sha256:ec5d40dd494882704fb876c16fa7261a69791e784ae34e6b5992e977bd2e238c",
+                "sha256:f633074f36dbc33d5c05b5dc75371e5660f1dbf9c8b1d95669def05e5425989c",
+                "sha256:f7fe3dbe871294ba70d789be16b6e7e52b418311e166e0e3cba9522f0f437fb1",
+                "sha256:f963ba8c3b0199f9d6b794c90ec77545e05eadc83973897a4523c9e8d84e9340"
             ],
-            "markers": "python_version >= '3.9'",
-            "version": "==21.0.0"
+            "index": "pypi",
+            "markers": "python_version >= '3.10'",
+            "version": "==22.0.0"
         },
         "pyarrow-stubs": {
             "hashes": [
-                "sha256:5389057a55db3c2662c05f22685a52e15e5effaf4345f41f12fb9b6b348647b9",
-                "sha256:e802b18e8e5fdf0a78afa05fae78f1456d861fcb1f95ec0234be5d6a5ecdcde2"
+                "sha256:09da6809f37cc6dbbbf59c9c8e42269290d19ac09f65d2b3456c671f1c3a8765",
+                "sha256:c0885c09f63e2be51bacb6b0e20b39083f43da1cb214d31e406f982e874bcb5a"
             ],
             "index": "pypi",
             "markers": "python_version >= '3.9' and python_version < '4'",
-            "version": "==20.0.0.20250928"
+            "version": "==20.0.0.20251107"
         },
         "pycparser": {
             "hashes": [
@@ -1649,20 +1669,20 @@
         },
         "pyparsing": {
             "hashes": [
-                "sha256:2df8d5b7b2802ef88e8d016a2eb9c7aeaa923529cd251ed0fe4608275d4105b6",
-                "sha256:e38a4f02064cf41fe6593d328d0512495ad1f3d8a91c4f73fc401b3079a59a5e"
+                "sha256:659d2246c7c8b35e95c9b28b24f2cf08617ff3c2aa6d6131ac0065a1ce49db1e",
+                "sha256:b94da344d6cb9db11f8870765917aa7c7c1a3b7cc93b4eb423a33c7a8c68bf2a"
             ],
             "markers": "python_version >= '3.9'",
-            "version": "==3.2.5"
+            "version": "==3.3.0a1"
         },
         "pytest": {
             "hashes": [
-                "sha256:86c0d0b93306b961d58d62a4db4879f27fe25513d4b969df351abdddb3c30e01",
-                "sha256:872f880de3fc3a5bdc88a11b39c9710c3497a547cfa9320bc3c5e62fbf272e79"
+                "sha256:3e9c069ea73583e255c3b21cf46b8d3c56f6e3a1a8f6da94ccb0fcf57b9d73c8",
+                "sha256:67be0030d194df2dfa7b556f2e56fb3c3315bd5c8822c6951162b92b32ce7dad"
             ],
             "index": "pypi",
-            "markers": "python_version >= '3.9'",
-            "version": "==8.4.2"
+            "markers": "python_version >= '3.10'",
+            "version": "==9.0.1"
         },
         "pytest-mock": {
             "hashes": [
@@ -1683,11 +1703,11 @@
         },
         "pytokens": {
             "hashes": [
-                "sha256:532d6421364e5869ea57a9523bf385f02586d4662acbcc0342afd69511b4dd43",
-                "sha256:74d4b318c67f4295c13782ddd9abcb7e297ec5630ad060eb90abf7ebbefe59f8"
+                "sha256:2f932b14ed08de5fcf0b391ace2642f858f1394c0857202959000b68ed7a458a",
+                "sha256:95b2b5eaf832e469d141a378872480ede3f251a5a5041b8ec6e581d3ac71bbf3"
             ],
             "markers": "python_version >= '3.8'",
-            "version": "==0.2.0"
+            "version": "==0.3.0"
         },
         "pyyaml": {
             "hashes": [
@@ -1794,29 +1814,29 @@
         },
         "ruff": {
             "hashes": [
-                "sha256:16b68e183a0e28e5c176d51004aaa40559e8f90065a10a559176713fcf435206",
-                "sha256:30a58c087aef4584c193aebf2700f0fbcfc1e77b89c7385e3139956fa90434e2",
-                "sha256:3ba9a8925e90f861502f7d974cc60e18ca29c72bb0ee8bfeabb6ade35a3abde7",
-                "sha256:4c63b2d99fafa05efca0ab198fd48fa6030d57e4423df3f18e03aa62518c565f",
-                "sha256:58e15bffa7054299becf4bab8a1187062c6f8cafbe9f6e39e0d5aface455d6b3",
-                "sha256:62ec8969b7510f77945df916de15da55311fade8d6050995ff7f680afe582c57",
-                "sha256:668fce701b7a222f3f5327f86909db2bbe99c30877c8001ff934c5413812ac02",
-                "sha256:703799d059ba50f745605b04638fa7e9682cc3da084b2092feee63500ff3d9b8",
-                "sha256:7450a243d7125d1c032cb4b93d9625dea46c8c42b4f06c6b709baac168e10543",
-                "sha256:7eb0499a2e01f6e0c285afc5bac43ab380cbfc17cd43a2e1dd10ec97d6f2c42d",
-                "sha256:838d1b065f4df676b7c9957992f2304e41ead7a50a568185efd404297d5701e8",
-                "sha256:a86bf575e05cb68dcb34e4c7dfe1064d44d3f0c04bbc0491949092192b515296",
-                "sha256:c958f66ab884b7873e72df38dcabee03d556a8f2ee1b8538ee1c2bbd619883dd",
-                "sha256:e41f785498bd200ffc276eb9e1570c019c1d907b07cfb081092c8ad51975bbe7",
-                "sha256:ea95da28cd874c4d9c922b39381cbd69cb7e7b49c21b8152b014bd4f52acddc2",
-                "sha256:eb732d17db2e945cfcbbc52af0143eda1da36ca8ae25083dd4f66f1542fdf82e",
-                "sha256:eec3bbbf3a7d5482b5c1f42d5fc972774d71d107d447919fca620b0be3e3b75e",
-                "sha256:f42c9495f5c13ff841b1da4cb3c2a42075409592825dada7c5885c2c844ac730",
-                "sha256:f8d07350bc7af0a5ce8812b7d5c1a7293cf02476752f23fdfc500d24b79b783c"
+                "sha256:2d1fa985a42b1f075a098fa1ab9d472b712bdb17ad87a8ec86e45e7fa6273e68",
+                "sha256:3676cb02b9061fee7294661071c4709fa21419ea9176087cb77e64410926eb78",
+                "sha256:410e781f1122d6be4f446981dd479470af86537fb0b8857f27a6e872f65a38e4",
+                "sha256:4b700459d4649e2594b31f20a9de33bc7c19976d4746d8d0798ad959621d64a4",
+                "sha256:6d146132d1ee115f8802356a2dc9a634dbf58184c51bff21f313e8cd1c74899a",
+                "sha256:7497d19dce23976bdaca24345ae131a1d38dcfe1b0850ad8e9e6e4fa321a6e19",
+                "sha256:88f0770d42b7fa02bbefddde15d235ca3aa24e2f0137388cc15b2dcbb1f7c7a7",
+                "sha256:8d3b48d7d8aad423d3137af7ab6c8b1e38e4de104800f0d596990f6ada1a9fc1",
+                "sha256:9d55d7af7166f143c94eae1db3312f9ea8f95a4defef1979ed516dbb38c27621",
+                "sha256:b595bedf6bc9cab647c4a173a61acf4f1ac5f2b545203ba82f30fcb10b0318fb",
+                "sha256:c01be527ef4c91a6d55e53b337bfe2c0f82af024cc1a33c44792d6844e2331e1",
+                "sha256:c135d4b681f7401fe0e7312017e41aba9b3160861105726b76cfa14bc25aa367",
+                "sha256:c83642e6fccfb6dea8b785eb9f456800dcd6a63f362238af5fc0c83d027dd08b",
+                "sha256:d93be8f1fa01022337f1f8f3bcaa7ffee2d0b03f00922c45c2207954f351f465",
+                "sha256:e2380596653dcd20b057794d55681571a257a42327da8894b93bbd6111aa801f",
+                "sha256:f3b8248123b586de44a8018bcc9fefe31d23dda57a34e6f0e1e53bd51fd63594",
+                "sha256:f55382725ad0bdb2e8ee2babcbbfb16f124f5a59496a2f6a46f1d9d99d93e6e2",
+                "sha256:f66e9bb762e68d66e48550b59c74314168ebb46199886c5c5aa0b0fbcc81b151",
+                "sha256:f7a75236570318c7a30edd7f5491945f0169de738d945ca8784500b517163a72"
             ],
             "index": "pypi",
             "markers": "python_version >= '3.7'",
-            "version": "==0.14.0"
+            "version": "==0.14.5"
         },
         "s3transfer": {
             "hashes": [
@@ -1875,19 +1895,19 @@
         },
         "types-awscrt": {
             "hashes": [
-                "sha256:66d77ec283e1dc907526a44511a12624118723a396c36d3f3dd9855cb614ce14",
-                "sha256:d88f43ef779f90b841ba99badb72fe153077225a4e426ae79e943184827b4443"
+                "sha256:15929da84802f27019ee8e4484fb1c102e1f6d4cf22eb48688c34a5a86d02eb6",
+                "sha256:2d453f9e27583fcc333771b69a5255a5a4e2c52f86e70f65f3c5a6789d3443d0"
             ],
             "markers": "python_version >= '3.8'",
-            "version": "==0.28.1"
+            "version": "==0.28.4"
         },
         "types-pytz": {
             "hashes": [
-                "sha256:222e32e6a29bb28871f8834e8785e3801f2dc4441c715cd2082b271eecbe21e5",
-                "sha256:4f55ed1b43e925cf851a756fe1707e0f5deeb1976e15bf844bcaa025e8fbd0db"
+                "sha256:0f1c9792cab4eb0e46c52f8845c8f77cf1e313cb3d68bf826aa867fe4717d91c",
+                "sha256:fca87917836ae843f07129567b74c1929f1870610681b4c92cb86a3df5817bdb"
             ],
             "markers": "python_version >= '3.9'",
-            "version": "==2025.2.0.20250809"
+            "version": "==2025.2.0.20251108"
         },
         "types-s3transfer": {
             "hashes": [
@@ -1915,11 +1935,11 @@
         },
         "virtualenv": {
             "hashes": [
-                "sha256:4f1a845d131133bdff10590489610c98c168ff99dc75d6c96853801f7f67af44",
-                "sha256:63d106565078d8c8d0b206d48080f938a8b25361e19432d2c9db40d2899c810a"
+                "sha256:643d3914d73d3eeb0c552cbb12d7e82adf0e504dbf86a3182f8771a153a1971c",
+                "sha256:c21c9cede36c9753eeade68ba7d523529f228a403463376cf821eaae2b650f1b"
             ],
             "markers": "python_version >= '3.8'",
-            "version": "==20.35.3"
+            "version": "==20.35.4"
         },
         "wcwidth": {
             "hashes": [
diff --git a/pyproject.toml b/pyproject.toml
index 1370ae7..c32f452 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -24,7 +24,7 @@ classifiers = [
 dependencies = [
     "attrs",
     "boto3",
-    "duckdb==1.4.2.dev27",
+    "duckdb==1.4.2",
     "duckdb_engine",
     "pandas",
     "pyarrow",
diff --git a/tests/conftest.py b/tests/conftest.py
index e77c1a4..8f1a2df 100644
--- a/tests/conftest.py
+++ b/tests/conftest.py
@@ -7,9 +7,10 @@
 import moto
 import pytest
 
-from tests.utils import generate_sample_records
+from tests.utils import generate_sample_embeddings, generate_sample_records
 from timdex_dataset_api import TIMDEXDataset, TIMDEXDatasetMetadata
 from timdex_dataset_api.dataset import TIMDEXDatasetConfig
+from timdex_dataset_api.embeddings import DatasetEmbedding
 from timdex_dataset_api.record import DatasetRecord
 
 
@@ -305,3 +306,19 @@ def _generate(num_records: int = 100, **kwargs) -> Iterator[DatasetRecord]:
         return generate_sample_records(num_records=num_records, **kwargs)
 
     return _generate
+
+
+@pytest.fixture
+def sample_embeddings() -> Iterator[DatasetEmbedding]:
+    """Generate 100 sample embeddings with default parameters."""
+    return generate_sample_embeddings(num_embeddings=100)
+
+
+@pytest.fixture
+def sample_embeddings_generator():
+    """Factory fixture for generating custom sample embeddings."""
+
+    def _generate(num_embeddings: int = 100, **kwargs) -> Iterator[DatasetEmbedding]:
+        return generate_sample_embeddings(num_embeddings=num_embeddings, **kwargs)
+
+    return _generate
diff --git a/tests/test_embeddings.py b/tests/test_embeddings.py
new file mode 100644
index 0000000..831eda2
--- /dev/null
+++ b/tests/test_embeddings.py
@@ -0,0 +1,130 @@
+# ruff: noqa: PLR2004
+import json
+import math
+import os
+from datetime import UTC, datetime
+
+import pyarrow.dataset as ds
+
+from timdex_dataset_api.embeddings import (
+    TIMDEX_DATASET_EMBEDDINGS_SCHEMA,
+    DatasetEmbedding,
+    TIMDEXEmbeddings,
+)
+
+
+def test_dataset_embedding_init():
+    values = {
+        "timdex_record_id": "alma:123",
+        "run_id": "test-run-1",
+        "run_record_offset": 0,
+        "embedding_model": "sentence-transformers/all-MiniLM-L6-v2",
+        "embedding_strategy": "full_record",
+        "timestamp": "2024-12-01T10:00:00+00:00",
+        "embedding_vector": [0.1, 0.2, 0.3],
+        "embedding_object": json.dumps(
+            {"token1": 0.1, "token2": 0.2, "token3": 0.3}
+        ).encode(),
+    }
+    embedding = DatasetEmbedding(**values)
+
+    assert embedding
+    assert embedding.timdex_record_id == "alma:123"
+    assert embedding.timestamp == datetime(2024, 12, 1, 10, 0, tzinfo=UTC)
+    assert embedding.embedding_object == b'{"token1": 0.1, "token2": 0.2, "token3": 0.3}'
+
+
+def test_dataset_embedding_date_properties():
+    embedding = DatasetEmbedding(
+        timdex_record_id="alma:123",
+        run_id="test-run-1",
+        run_record_offset=0,
+        embedding_model="sentence-transformers/all-MiniLM-L6-v2",
+        embedding_strategy="full_record",
+        timestamp="2024-12-01T10:00:00+00:00",
+        embedding_vector=[0.1, 0.2, 0.3],
+    )
+
+    assert (embedding.year, embedding.month, embedding.day) == ("2024", "12", "01")
+
+
+def test_dataset_embedding_to_dict():
+    values = {
+        "timdex_record_id": "alma:123",
+        "run_id": "test-run-1",
+        "run_record_offset": 0,
+        "embedding_model": "sentence-transformers/all-MiniLM-L6-v2",
+        "embedding_strategy": "full_record",
+        "timestamp": "2024-12-01T10:00:00+00:00",
+        "embedding_vector": [0.1, 0.2, 0.3],
+        "embedding_object": None,
+    }
+    embedding = DatasetEmbedding(**values)
+    embedding_dict = embedding.to_dict()
+
+    assert embedding_dict["timdex_record_id"] == "alma:123"
+    assert embedding_dict["year"] == "2024"
+    assert embedding_dict["month"] == "12"
+    assert embedding_dict["day"] == "01"
+    assert embedding_dict["embedding_vector"] == [0.1, 0.2, 0.3]
+
+
+def test_embeddings_data_root_property(timdex_dataset_empty):
+    timdex_embeddings = TIMDEXEmbeddings(timdex_dataset_empty)
+
+    expected = f"{timdex_dataset_empty.location.removesuffix('/')}/data/embeddings"
+    assert timdex_embeddings.data_embeddings_root == expected
+
+
+def test_embeddings_write_basic(timdex_dataset_empty, sample_embeddings_generator):
+    timdex_embeddings = TIMDEXEmbeddings(timdex_dataset_empty)
+    written_files = timdex_embeddings.write(sample_embeddings_generator(100))
+
+    assert len(written_files) == 1
+    assert os.path.exists(written_files[0].path)
+
+    # verify written data can be read
+    dataset = ds.dataset(
+        timdex_embeddings.data_embeddings_root, format="parquet", partitioning="hive"
+    )
+    assert dataset.count_rows() == 100
+
+
+def test_embeddings_write_partitioning(timdex_dataset_empty, sample_embeddings_generator):
+    timdex_embeddings = TIMDEXEmbeddings(timdex_dataset_empty)
+    written_files = timdex_embeddings.write(sample_embeddings_generator(10))
+
+    assert len(written_files) == 1
+    assert "year=2024/month=12/day=01" in written_files[0].path
+
+
+def test_embeddings_write_schema_applied(
+    timdex_dataset_empty, sample_embeddings_generator
+):
+    timdex_embeddings = TIMDEXEmbeddings(timdex_dataset_empty)
+    timdex_embeddings.write(sample_embeddings_generator(10))
+
+    # manually load dataset to confirm schema
+    dataset = ds.dataset(
+        timdex_embeddings.data_embeddings_root,
+        format="parquet",
+        partitioning="hive",
+    )
+
+    assert set(dataset.schema.names) == set(TIMDEX_DATASET_EMBEDDINGS_SCHEMA.names)
+
+
+def test_embeddings_create_batches(timdex_dataset_empty, sample_embeddings_generator):
+    timdex_embeddings = TIMDEXEmbeddings(timdex_dataset_empty)
+    total_embeddings = 101
+    timdex_dataset_empty.config.write_batch_size = 50
+
+    batches = list(
+        timdex_embeddings.create_embedding_batches(
+            sample_embeddings_generator(total_embeddings)
+        )
+    )
+
+    assert len(batches) == math.ceil(
+        total_embeddings / timdex_dataset_empty.config.write_batch_size
+    )
diff --git a/tests/utils.py b/tests/utils.py
index 5f455a2..f6de49b 100644
--- a/tests/utils.py
+++ b/tests/utils.py
@@ -2,11 +2,13 @@
 
 # ruff: noqa: S311
 
+import json
 import random
 import uuid
 from collections.abc import Iterator
 
 from timdex_dataset_api import DatasetRecord
+from timdex_dataset_api.embeddings import DatasetEmbedding
 
 
 def generate_sample_records(
@@ -58,3 +60,37 @@ def generate_sample_records_with_simulated_partitions(
             action=random.choice(actions),
         )
         records_remaining -= batch_size
+
+
+def generate_sample_embeddings(
+    num_embeddings: int,
+    source: str | None = "alma",
+    embedding_model: str | None = "super-org/amazing-model",
+    embedding_strategy: str | None = "full_record",
+    run_id: str | None = None,
+    timestamp: str | None = "2024-12-01T00:00:00+00:00",
+) -> Iterator[DatasetEmbedding]:
+    """Generate sample DatasetEmbeddings."""
+    if not run_id:
+        run_id = str(uuid.uuid4())
+
+    for x in range(num_embeddings):
+        embedding_vector = [random.random() for _ in range(768)]
+        embedding_object = json.dumps(
+            {
+                "token1": 0.1,
+                "token2": 0.2,
+                "token3": 0.3,
+            }
+        ).encode()
+
+        yield DatasetEmbedding(
+            timdex_record_id=f"{source}:{x}",
+            run_id=run_id,
+            run_record_offset=x,
+            embedding_model=embedding_model,
+            embedding_strategy=embedding_strategy,
+            timestamp=timestamp,
+            embedding_vector=embedding_vector,
+            embedding_object=embedding_object,
+        )
diff --git a/timdex_dataset_api/__init__.py b/timdex_dataset_api/__init__.py
index b60fae2..e2da0b5 100644
--- a/timdex_dataset_api/__init__.py
+++ b/timdex_dataset_api/__init__.py
@@ -4,7 +4,7 @@
 from timdex_dataset_api.metadata import TIMDEXDatasetMetadata
 from timdex_dataset_api.record import DatasetRecord
 
-__version__ = "3.5.0"
+__version__ = "3.6.0"
 
 __all__ = [
     "DatasetRecord",
diff --git a/timdex_dataset_api/embeddings.py b/timdex_dataset_api/embeddings.py
new file mode 100644
index 0000000..56e2213
--- /dev/null
+++ b/timdex_dataset_api/embeddings.py
@@ -0,0 +1,183 @@
+import itertools
+import logging
+import time
+import uuid
+from collections.abc import Iterator
+from datetime import UTC, datetime
+
+import attrs
+import pyarrow as pa
+import pyarrow.dataset as ds
+from attrs import asdict, define, field
+
+from timdex_dataset_api import TIMDEXDataset
+from timdex_dataset_api.record import datetime_iso_parse
+
+logger = logging.getLogger(__name__)
+
+TIMDEX_DATASET_EMBEDDINGS_SCHEMA = pa.schema(
+    (
+        pa.field("timdex_record_id", pa.string()),
+        pa.field("run_id", pa.string()),
+        pa.field("run_record_offset", pa.int32()),
+        pa.field("timestamp", pa.timestamp("us", tz="UTC")),
+        pa.field("embedding_model", pa.string()),
+        pa.field("embedding_strategy", pa.string()),
+        pa.field("embedding_vector", pa.list_(pa.float32())),
+        pa.field("embedding_object", pa.binary()),
+        pa.field("year", pa.string()),
+        pa.field("month", pa.string()),
+        pa.field("day", pa.string()),
+    )
+)
+
+
+@define
+class DatasetEmbedding:
+    """Container for single record embedding.
+
+    Fields:
+        timdex_record_id: Fields (timdex_record_id, run_id, run_record_offset) combine to
+            form a composite key that points to a single, distinct record version in the
+            records data.
+        run_id: ...
+        run_record_offset: ...
+        embedding_model: Embedding model name, e.g. HuggingFace URI
+        embedding_strategy: Strategy used to create embedding
+            - this correlates to a transformation strategy in the timdex-embeddings CLI
+            application, e.g. "full_record"
+        timestamp: Timestamp when embedding was created
+        embedding_vector: Numerical vector representation of embedding
+            - preferred form for storing embedding as a numerical array
+        embedding_object: Object representation of the embedding
+            - example: {token:weight, ...} representation for sparse vector
+            - flexible enough to hold other representations
+    """
+
+    timdex_record_id: str = field()
+    run_id: str = field()
+    run_record_offset: int = field()
+    embedding_model: str = field()
+    embedding_strategy: str = field()
+    timestamp: datetime = field(  # type: ignore[assignment]
+        converter=datetime_iso_parse,
+        default=attrs.Factory(lambda: datetime.now(tz=UTC).isoformat()),
+    )
+    embedding_vector: list[float] = field(default=None)
+    embedding_object: bytes = field(default=None)
+
+    @property
+    def year(self) -> str:
+        return self.timestamp.strftime("%Y")
+
+    @property
+    def month(self) -> str:
+        return self.timestamp.strftime("%m")
+
+    @property
+    def day(self) -> str:
+        return self.timestamp.strftime("%d")
+
+    def to_dict(
+        self,
+    ) -> dict:
+        """Serialize instance as dictionary."""
+        return {
+            **asdict(self),
+            "year": self.year,
+            "month": self.month,
+            "day": self.day,
+        }
+
+
+class TIMDEXEmbeddings:
+
+    def __init__(self, timdex_dataset: TIMDEXDataset):
+        """Init TIMDEXEmbeddings.
+
+        Class to handle the writing and readings of embeddings associated with TIMDEX
+        records.
+
+        Args:
+            - timdex_dataset: instance of TIMDEXDataset
+        """
+        self.timdex_dataset = timdex_dataset
+
+        self.schema = TIMDEX_DATASET_EMBEDDINGS_SCHEMA
+        self.partition_columns = ["year", "month", "day"]
+
+    @property
+    def data_embeddings_root(self) -> str:
+        return f"{self.timdex_dataset.location.removesuffix('/')}/data/embeddings"
+
+    def write(
+        self,
+        embeddings_iter: Iterator[DatasetEmbedding],
+        *,
+        use_threads: bool = True,
+    ) -> list[ds.WrittenFile]:
+        """Write embeddings as parquet files to /data/embeddings.
+
+        Approach is similar to TIMDEXDataset.write() for Records:
+            - use self.data_embeddings_root for location of embeddings parquet files
+            - use pyarrow Dataset to write rows
+        """
+        start_time = time.perf_counter()
+        written_files: list[ds.WrittenFile] = []
+
+        filesystem, path = self.timdex_dataset.parse_location(self.data_embeddings_root)
+
+        embedding_batches_iter = self.create_embedding_batches(embeddings_iter)
+        ds.write_dataset(
+            embedding_batches_iter,
+            base_dir=path,
+            basename_template="%s-{i}.parquet" % (str(uuid.uuid4())),  # noqa: UP031
+            existing_data_behavior="overwrite_or_ignore",
+            filesystem=filesystem,
+            file_visitor=lambda written_file: written_files.append(written_file),  # type: ignore[arg-type]
+            format="parquet",
+            max_open_files=500,
+            max_rows_per_file=self.timdex_dataset.config.max_rows_per_file,
+            max_rows_per_group=self.timdex_dataset.config.max_rows_per_group,
+            partitioning=self.partition_columns,
+            partitioning_flavor="hive",
+            schema=self.schema,
+            use_threads=use_threads,
+        )
+
+        self.log_write_statistics(start_time, written_files)
+
+        return written_files
+
+    def create_embedding_batches(
+        self, embeddings_iter: Iterator["DatasetEmbedding"]
+    ) -> Iterator[pa.RecordBatch]:
+        for i, embedding_batch in enumerate(
+            itertools.batched(
+                embeddings_iter, self.timdex_dataset.config.write_batch_size
+            )
+        ):
+            embedding_dicts = [embedding.to_dict() for embedding in embedding_batch]
+            batch = pa.RecordBatch.from_pylist(embedding_dicts)
+            logger.debug(f"Yielding batch {i + 1} for dataset writing.")
+            yield batch
+
+    def log_write_statistics(
+        self,
+        start_time: float,
+        written_files: list[ds.WrittenFile],
+    ) -> None:
+        """Parse written files from write and log statistics."""
+        total_time = round(time.perf_counter() - start_time, 2)
+        total_files = len(written_files)
+        total_rows = sum(
+            [wf.metadata.num_rows for wf in written_files]  # type: ignore[attr-defined]
+        )
+        total_size = sum([wf.size for wf in written_files])  # type: ignore[attr-defined]
+        logger.info(
+            f"Dataset write complete - elapsed: "
+            f"{total_time}s, "
+            f"total files: {total_files}, "
+            f"total rows: {total_rows}, "
+            f"total size: {total_size}"
+        )